Caregivers’ perceptions, challenges and service needs related to tackling childhood overweight and obesity: a qualitative study in three districts of Shanghai, China
Bibliographic record
Abstract
BACKGROUND: Childhood overweight and obesity (OWO) has become a major public concern worldwide including in Shanghai, one of the most developed areas of China. Understanding perceptions and challenges of tackling childhood OWO among caregivers of children is critical to provide services in need. METHODS: A qualitative descriptive study including in-depth interviews with seven parents and six focus group discussions with a total of 32 parents or grandparents of children zero to 6 years of age. Participants lived in three districts of Shanghai and indexed children included both those with OWO or non-OWO children. Data were analyzed using qualitative thematic analysis. RESULTS: Caregivers tended to underestimate children's weight status, and to regard chubby children as a sign of good parental care. Some caregivers even suggested that there were positive effects of childhood overweight. Caregivers identified a number of challenges to prevention of OWO in children, including difficulties in controlling dietary intake or increasing children's physical activities; discordant views between parents and grandparents, and barriers to accessing professional guidance. Caregivers desired more detailed advice regarding children's nutrition intake and physical activity, and preferred online approaches. CONCLUSIONS: Misconceptions regarding childhood overweight were found in caregivers of children in Shanghai. Professional guidance on childhood weight control for caregivers is desired via digital applications such as mobile phone applications and social media.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".